Abstract
Research into betrayal ranges from case studies of real-world betrayers to controlled laboratory experiments. However, the capability of reliably detecting individuals who have previously betrayed through an analysis of their ongoing behavior (after the act of betrayal) has not been studied. To this aim, we propose a novel method composed of a game and several manipulations to stimulate and heighten emotions related to betrayal. We discuss the results of using this game and the manipulations as a mechanism to spot betrayers, with the goal of identifying important manipulations that can be used in future studies to detect betrayers in real-world contexts. In this paper, we discuss the methods and results of modeling the collected game data, which include behavioral logs, to identify betrayers. We used several analysis methods based both on psychology-based hypotheses as well as machine learning techniques. Results show that stimuli that target engagement, persistence, feedback to teammates, and team trust produce behaviors that can contribute to distinguishing betrayers from non-betrayers.
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Acknowledgments
The research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract number 2016-16031100003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
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Rizzo, P., Jemmali, C., Leung, A., Haigh, K., El-Nasr, M.S. (2018). Detecting Betrayers in Online Environments Using Active Indicators. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_2
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